nep-lma 2026-05-04 papers

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By:	Freund, Lukas (Boston College); Mann, Lukas (Arizona State University)
Abstract:	A central effect of automation is to transform jobs - shifting their task content. We develop a general-equilibrium model of this process. Occupations bundle tasks; workers possess task-specific skills and sort by comparative advantage. When a task is automated, remaining tasks gain in importance, so wage effects depend on workers' full skill profiles. We estimate the distribution of task-specific skills and project individual-level wage effects of generative AI automation. Moderate exposure benefits workers on average but high exposure harms them, with large dispersion within occupations; the return to social skills rises, that to analytical skills falls; and low-earners gain more than high-earners. Job transformation drives these results.
Keywords:	AI, bundling, labor markets, skills, task model
JEL:	E24 J23 J24 J31 O33
Date:	2026–04
URL:	https://d.repec.org/n?u=RePEc:iza:izadps:dp18565

By:	Astruc--Le Souder, Mael (Bordeaux University); Bargain, Olivier (University of Bordeaux); Locks, Gedeao (DIW, Berlin)
Abstract:	As tertiary education expands, employers increasingly rely on academic distinctions to screen among similarly qualified graduates. We study the labor-market effects of honors using administrative and survey data on Sorbonne master's graduates. We exploit France's fixed GPA thresholds for honors assignment to implement a fuzzy regression discontinuity design. Returns are concentrated at the intermediate distinction ("High Honors"), indicating that credentials are most informative when they separate above- from below-average students. We find that High Honors accelerate school-to-work transitions, increasing the monthly job-finding rate by about 40%. Honors also generate an initial wage premium, which fades within two years, and lead to persistent improvements in job quality, including greater access to master's-level positions and faster transitions to permanent contracts. These results highlight the role of academic distinctions as short-run signals that shape early career allocation rather than long-term earnings.
Keywords:	signaling, honors, regression discontinuity design, fuzzy RDD
JEL:	J23 J24 J31 I23 I28
Date:	2026–04
URL:	https://d.repec.org/n?u=RePEc:iza:izadps:dp18567

By:	Anders Humlum; Emilie Vestergaard
Abstract:	We study the early labor market impacts of AI chatbots by linking large-scale adoption surveys to administrative labor market records in Denmark. We document rapid currents: most employers in exposed occupations have adopted chatbot initiatives, workers report productivity benefits, and new AI-related tasks are widespread. Yet these currents have not broken the surface: using difference-in-differences, we estimate precise null effects on earnings and recorded hours at both the worker and workplace levels, ruling out effects larger than 2% two years after the launch of ChatGPT. What moves is the structure of work: employers absorb AI through task reorganization-including new tasks in content generation, AI oversight, and AI integration-and adopters transition into higher-paying occupations where AI chatbots are more relevant, though still too few to move average earnings. Technological change reshapes work well before it surfaces in earnings or hours.
Keywords:	Generative AI; Labor Markets
JEL:	J23 J24 J31 O33
Date:	2026–03
URL:	https://d.repec.org/n?u=RePEc:crm:wpaper:26078

By:	Michelle Yin; Hoa Vu; Claudia Persico
Abstract:	A rapidly growing literature estimates AI's labor-market effects using large language models (LLMs) to self-assess occupational exposure. We demonstrate these measures are highly fragile. Replicating the dominant rubric with three frontier models on identical tasks, we find a 3.6-fold divergence in mean exposure with agreement as low as 57%. This measurement instability alters downstream empirical conclusions: in a difference-in-differences framework, individual-level coefficient magnitudes vary 2.4-fold across annotators, and county level estimates flip from a significant negative to an insignificant positive depending on annotators. We formalize this non-classical measurement error, highlighting the risks of treating evolving LLMs as static instruments.
JEL:	C81 J23 J24 O33
Date:	2026–04
URL:	https://d.repec.org/n?u=RePEc:nbr:nberwo:35110

By:	Michael Amior; Shmuel San
Abstract:	Firms face significant constraints in their ability to differentiate pay by worker productivity. We show how these internal equity constraints generate a quantity-quality trade-off in hiring: firms which offer higher wages attract higher skilled workers, but cannot profitably employ lower skilled workers. In equilibrium, this results in workplace segregation and pay dispersion even among ex-ante identical firms. Our framework provides a novel interpretation of the (empirically successful) log additive AKM wage model, and shows how log additivity can be reconciled with sorting of high-skilled workers to high-paying firms. It can also rationalize a hump-shaped relationship between firm size and firm pay, and provides new insights into aggregate-level, regional and sectoral variation in earnings inequality-which we explore using Israeli administrative data.
Keywords:	Wage-setting, monopsony, internal pay equity, workplace segregation, firm size
JEL:	J31 J42
Date:	2026–02
URL:	https://d.repec.org/n?u=RePEc:crm:wpaper:26052

By:	Pascuel Plotkin
Abstract:	This paper studies how firm adoption of digital technologies reshapes labor demand and worker earnings. Linking administrative employer-employee records to restaurants and workers from a major delivery platform and using a matched event-study, I show that adopting restaurants substitute in-house labor hours one-for-one with outsourced platform-worker hours. Earnings losses for incumbent workers are modest because displaced workers reallocate to new formal-sector jobs. Exposed non-adopting restaurants are more likely to close, and their workers experience larger losses. I quantify earnings effects across restaurant and platform workers, showing how platform adoption redistributes earnings across workers and creates income outside traditional restaurant employment.
Keywords:	Alternative work arrangements, gig economy, technological change, outsourcing, displacement, informality
JEL:	J23 J31 J46 O33 L86 J63
Date:	2026–03
URL:	https://d.repec.org/n?u=RePEc:crm:wpaper:26067

By:	Suphanit Piyapromdee; Tasina Tawichsri; Nada Wasi
Abstract:	This paper studies Thailand's 2012-2013 nationwide minimum-wage reform, which raised wage floors by over 40 percent. Using matched employer-employee data, we study its effects on earnings, employment dynamics, and worker-firm sorting. We estimate a discrete type model that jointly captures heterogeneity in wages and mobility across workers and firms. Earnings rise sharply at the bottom with spillovers well above the new minimum, while employment effects are modest and concentrated among the long-term non-employed. Simulations imply sizable gains in discounted lifetime earnings, driven mainly by higher wages but amplified by mobility changes for high-turnover workers. The reform also alters career wage profiles: entry wages increase for low- and mid-wage workers, but tenure-based wage growth flattens most for mid-wage workers, generating an intertemporal trade-off between higher starting pay and slower subsequent progression. Finally, assortative matching weakens as lower-type workers move up the firm wage ladder, yet revealed-preference measures show that wage-based upgrading does not always translate into higher-valued jobs.
Keywords:	Minimum wage, worker-firm sorting, job mobility, wage dynamics, matched employer- employee data, revealed preferences, lifetime earnings
JEL:	J31 J38 J60 J64
Date:	2026–03
URL:	https://d.repec.org/n?u=RePEc:crm:wpaper:26060

By:	Rune Vejlin; Jonas Maibom; Malthe Elholm; Jesper Bagger
Abstract:	Using Using Danish matched employer-employee data from 1980-2019, this paper shows that rising wage sorting - the correlation between worker and firm wage fixed effects - increased from 0.06 to 0.18 and is driven entirely by employment shifting toward firms that consistently form high‑sorting matches. Individual firms' sorting propensities remain stable. Decomposition reveals that 60% of the increase reflects reallocation among surviving firms, while 40% stems from firm entry and exit. Regression analysis highlights firm turnover and industry shifts as the primary drivers, with rising educational attainment contributing through the concentration of educated workers in high‑sorting firms. Job‑to‑job mobility is the main reallocation mechanism.
Keywords:	Inequality, sorting, firm dynamics, firm entry, firm exit, matched employer-employee data
JEL:	E24 J21 J31
Date:	2026–03
URL:	https://d.repec.org/n?u=RePEc:crm:wpaper:26095

By:	Arnaud Dupuy; Morgan Raux; Sara Signorelli
Abstract:	Technological change affects labor markets not only by shifting labor demand across occupations, but also by reshaping the skill distances that govern workers' ability to move between jobs. This paper studies the digitalization wave of the 2010s using task data from online job postings, matched employer-employee data, and a gravity framework of occupational mobility. We show that while most occupations became more digital, skill distances converged for some occupation pairs and diverged for others, increasing mobility along some pathways and reducing it along others. Counterfactual simulations show that these frictions are meaningful and slow reallocation out of shrinking occupations.
Keywords:	Occupation mobility, Technological change, Matching
JEL:	J23 J24 J62
Date:	2026–03
URL:	https://d.repec.org/n?u=RePEc:crm:wpaper:26062

By:	Clara von Bismarck-Osten
Abstract:	Is the public sector losing the most educated to finance, tech, and consulting? Does it act as a Keynesian employment insurer to the less educated? The data constraints behind the limited evidence on this have recently been overcome for England. The probability of entering the public sector rises with education, and this paper documents that this educational gradient has strengthened. Relative to just before the financial crisis, the public sector became a less common workplace for individuals entering the labour market with low levels of education. At the same time, it became a more common destination for the most educated, whether defined by highest qualification, university rank, or final grade. Graduates from top universities do show increased entry into finance, the tech sector, and the consulting industry, but also show-despite a substantial deterioration of pay differentials-an increasing preference for the public sector as an entry-workplace. A decomposition of the trends by graduate discipline and destination industry points to UK-specific policy explanations.
Keywords:	Public Sector, Occupational Choice
JEL:	J24 J45
Date:	2026–03
URL:	https://d.repec.org/n?u=RePEc:crm:wpaper:26094

By:	Guillermo Cruces; Diego Fernández Meijide; Sebastian Galiani; Ramiro H. Gálvez; María Lombardi
Abstract:	Does generative artificial intelligence (AI) reinforce or reduce productivity differences across workers? Existing evidence largely studies AI within firms and occupations, where organizationalselectioncompresseseducationalheterogeneity, leavingunclearwhetherAI narrows productivity gaps across individuals with substantially different levels of formal education. Weaddressthisquestionusingarandomizedonlineexperimentconductedoutside firms, in which1, 174 adults aged 25–45 with heterogeneous educational backgrounds complete an incentivized, workplace-style business problem-solving task. The task is a general (not domain-specific) exercise, and participants perform it either with or without access to a generative-AI assistant. Unlike prior work that studies heterogeneity within relatively homogeneous worker samples, our designtargets the between–education-group productivity gap as the primary estimand. We find that AI increases productivity for all participants, with substantially larger gains for lower-education individuals. In the absence of AIaccess, higher-education participants outperform lower-education participants by0.548standarddeviations; withAIaccess, thisgapfallsto0.139standarddeviations, implying that generative AI closes three-quarters of the initial productivity gap. We interpret this pattern as evidence that generative AI narrows effective productivity differences in task execution by relaxing constraints that are more binding for lower-education individuals, even though underlying skill differences remain, as reflected in persistent education gaps in task performance and in a follow-up exercise without AI assistance.
Keywords:	Productivity, artificial intelligence, education, human capital, inequality
JEL:	J24 O33
Date:	2026–03
URL:	https://d.repec.org/n?u=RePEc:udt:wpgobi:wp_gob_2026_03

By:	Claudio Schilter; Samuel Lüthi; Stefan C. Wolter
Abstract:	We merge experimental data on competitiveness of a large sample of students with their complete educational history for up to ten years after the initial assessment. Exploiting quasi-random class assignments and controlling for other non-cognitive peer characteristics, we find that having competitive peers as classmates makes students choose and secure positions in higher-paying occupations. These occupations are also more challenging and-among male students-more popular. On the cost side, competitive peers do not lead to a lower probability of graduating from the subsequent job-specific education, but they significantly increase the probability of requiring extra time to do so.
Keywords:	Peer effects, competitiveness, occupational choice
JEL:	C93 D91 J24
Date:	2026–02
URL:	https://d.repec.org/n?u=RePEc:crm:wpaper:26058

By:	Fenella Carpena; Simon Galle
Abstract:	Motivated by the rise of artificial intelligence (AI), we set up a quantitative general-equilibrium model of the labor market impact of occupation-specific technical change. The highly tractable model crystallizes how three fundamental forces shape the impact of technical change on the occupational wage distribution: the input substitution elasticity, the final demand elasticity, and the labor supply (reallocation) elasticity. The difference between the former two elasticities determines whether machines and workers are gross complements, while the reallocation elasticity governs the magnitude of the distributional effects. We estimate the reallocation elasticities from group-by-occupation specialization changes, allowing for asymmetric reallocation and associated ripple effects on wages across occupations. After combining these estimates with externally disciplined demand and substitution parameters, as well as AI exposure measures, we shed light on the aggregate and distributional effects of occupation-specific advances in AI: wages in administrative services grow the least, ripple effects on less exposed occupations are substantial, AI modestly compresses the returns to education, and, on average, disproportionately benefits lower-income groups.
Keywords:	Technical change, labor reallocation, wage inequality, occupational heterogeneity, artificial intelligence, large language models
JEL:	J23 J24 E24 E25
Date:	2026–02
URL:	https://d.repec.org/n?u=RePEc:crm:wpaper:26039

By:	Cassandra Merrit (Keough School of Global Affairs, University of Notre Dame); Jacob Dominski (Institute for Ethics and the Common Good, University of Notre Dame); Christopher Hoy (Melbourne Institute of Applied Economic and Social Research)
Abstract:	Artificial intelligence (AI) is frequently cast as a transformative technology that will raise productivity while displacing human work, yet organizational adoption remains uneven and aggregate effects are mixed. We examine whether middle managers contribute to this gap by acting as gatekeepers to AI adoption. In a pre-registered survey experiment of 2, 000 managers in the United States and United Kingdom, respondents were randomly assigned to view videos summarizing recent evidence on AI’s productivity benefits, its labor-displacing potential, or a placebo control. Exposure to information about labor displacement leads to a large reduction in intended AI adoption and advocacy (by 0.4–0.5 standard deviations) and a moderate reduction in staffing intentions (by 0.2 standard deviations). In contrast, information about productivity benefits has no significant average effect, although it increases advocacy among managers with low prior familiarity with AI. These findings indicate that middle managers’ responses to the information environment shape both technology adoption and employment intentions. Rather than inducing substitution away from labor and toward AI, information about AI’s labor-displacing potential leads managers to scale back both planned AI adoption and their staffing intentions.
Keywords:	Artificial Intelligence, Technology adoption, Managers, Gatekeepers, Productivity, Labor displacement
JEL:	J23 J24 O33 L2 M54 D83
Date:	2026–04
URL:	https://d.repec.org/n?u=RePEc:iae:iaewps:wp2026n02

By:	Todd Morris; Stefan Staubli; Benoit Dostie
Abstract:	We evaluate the welfare effects of five provincial mandatory retirement bans in Canada from 2005 to 2009 using linked employer-employee tax data. The bans sharply reduce retirements at age 65, with sizable announcement effects and heterogeneity across industries. Post-65 employment and earnings rise at least 14%, with gains comparable to a two-year increase in pension-eligibility ages. Older workers save more and spouses postpone retirement, benefiting public finances, with no observable effects on mortality or younger workers. Highly exposed firms reduce payroll costs via hiring adjustments while maintaining worker productivity and profitability. Our results suggest that protecting older workers was welfare-improving.
JEL:	H55 J26 J78
Date:	2026–04
URL:	https://d.repec.org/n?u=RePEc:nbr:nberwo:35111

By:	Estefan, Alejandro (University of Notre Dame); Gerhard, Roberto (Secretaría del Trabajo y Previsión Social); Kaboski, Joseph (University of Notre Dame, CEPR, NBER); Kondo, Illenin (Federal Reserve Bank of Minneapolis); Qian, Wei (Haverford College)
Abstract:	Using Mexican economic census data from 1994 to 2019, we document a rising trend in domestic outsourcing, particularly among large firms, and a negative association between outsourcing and labor compensation, including profit sharing and social security. We leverage higher-frequency data from a manufacturing panel survey, matched employer-employee data, and a ban on domestic outsourcing in 2021 to show that the ban reduced outsourcing, increased labor's share, and reduced markdowns without raising total labor costs or affecting employment, output, or productivity. We propose a theoretical model in which corporate fiscal incentives drive outsourcing and account for the observed empirical patterns.
Keywords:	markdowns, monopsony, outsourcing, developing countries
JEL:	J38 J42 J81 M55 O15
Date:	2026–04
URL:	https://d.repec.org/n?u=RePEc:iza:izadps:dp18566

By:	Pedro C. Sant'Anna; Sulin Sardoschau; Aiko Schmeisser
Abstract:	Empirical studies of racial wage disparities typically rely on self-reported race and treat racial categories as fixed. This paper shows that racial classification in the labor market is produced by social perception, and that modeling this perception process is essential for measuring wage gaps. We combine two large-scale administrative data sets to construct three racial identity measures for 330, 000 workers in Brazil between 2003 and 2015: employer classification, self-identification, and an algorithmic skin-tone measure extracted from photographs. In over 20 percent of cases, self-identified and employer-ascribed race do not match, and employers disagree in their classification of the same worker. To quantify how race is constructed, we estimate a "race function" describing how employers map phenotypic cues, self-identification, local context, education, and employment histories into racial categories, showing that productivity-relevant factors shape perceptions. Holding skin tone constant, university graduates are 10 percentage points more likely to be perceived as White. Education whitens even conditional on self-declared race and within firm-by-occupation cells. Measured wage disparities differ depending on whether race is self-reported, employer-ascribed, or skin-tone based, and accounting for racial perceptions substantially changes estimated wage gaps. We show that conventional approaches overstate the role of productivity differences in explaining racial wage gaps.
Keywords:	Race, identity, disparity, wage gap, Brazil
JEL:	J15 J50 J71 Z10
Date:	2026–03
URL:	https://d.repec.org/n?u=RePEc:crm:wpaper:26074

By:	Michael A. Clemens
Abstract:	The US government in 2025 imposed a $100, 000 tax on each high-skill foreign worker entering with an H-1B work visa. The only public economic justification calculates the tax to offset an estimated wage penalty for H-1B workers relative to US natives. But this estimate suffers from substantial bias. Reexamining the same data shows that H-1B workers receive a modest wage premium relative to comparable natives, roughly 6 percent on average-inconsistent with any wage penalty-when using equivalent wage concepts and comparing workers of the same age, gender, education, and tenure, in the same occupation and local labor market. I trace most of the discrepancy to four methodological choices that inflate the prior estimate: 1) undisclosed imputation of missing data, 2) pooling of non-contemporaneous years, 3) a definition of local labor markets contradicting standard economic practice and US law, and 4) failure to consider H-1B workers' low job tenure. The remaining discrepancy arises from comparing incompatible wage concepts for H-1B versus native workers. Beyond measurement, the theory of public economics implies that a revenue-maximizing immigration tax reduces welfare relative to alternative policies, even with zero weight for immigrant welfare.
Keywords:	migration, migrant, immigrant, immigration, earnings, wages, taxes, tariffs, barriers, restrictions, skill, skilled, h-1b, welfare, native, citizen, college, stem, worker, foreign, labor, labour
JEL:	J08 J38 J68 H21
Date:	2026–03
URL:	https://d.repec.org/n?u=RePEc:crm:wpaper:26072

By:	Jaime Arellano-Bover; Carolina Bussotti; Matteo Paradisi; Liangjie Wu
Abstract:	Brand capital-an intangible asset that differentiates a firm's products-has grown in recent decades, alongside the rise of intangible investments and the decline in the labor share. Trademarks are legal claims on brand capital and are actively traded across firms, providing a setting to study how reallocating brand capital reshapes firm behavior and aggregate outcomes. Leveraging a novel link of Italian administrative data on trademark ownership, firms' financial statements, and employer-employee records, we exploit firm-to-firm trademark transactions to identify the effects of brand-capital investments. Guided by a model in which firms combine production labor, expansionary labor, and brand capital, we use an event-study design to estimate firm-level effects and quantify their aggregate implications. Acquiring a trademark increases intangible assets by 19%, sales by 8%, and employment by 6%, while leaving weekly earnings unchanged and reducing the firm-level labor share. Employment gains are concentrated among marketing and sales workers, indicating that brand capital is not skill-neutral. Accounting for both buyers and sellers, trademark transactions reallocate brand capital toward larger firms, raising sales and lowering the labor share. Calibrating the model to our estimates, we find that this reallocation generates a one percentage-point decline in the aggregate labor share in the long run.
Keywords:	Brand capital, trademarks, labor share, labor demand, markups
JEL:	L25 O34 E25 J23
Date:	2026–03
URL:	https://d.repec.org/n?u=RePEc:crm:wpaper:26079

By:	Francesca Miserocchi; Savannah Noray; Alice Wu
Abstract:	The advances of artificial intelligence (AI) are built on the groundwork laid by researchers. We study the labor market competition between academia and industry for AI researchers and its consequences for public knowledge production. Using data on 150, 000 computer science researchers, we document a major reallocation of AI talent toward top technology firms between 2005 and 2020. Publications at AI conferences predict transitions to top firms more strongly than to academia. Exploiting acceptance decisions at a leading AI conference, we compare accepted authors with similar rejected authors and find that a publication increases the probability of moving to a top firm by 2-6 percentage points in the next 1-3 years. Sorting to top firms is stronger for male researchers, whereas female students and postdocs are more likely to get tenure-track positions following a publication. Researchers who move to top firms subsequently publish fewer papers, resulting in approximately 1, 000 fewer AI papers and 2, 000 fewer papers in other computer science areas per year in the public domain.
Keywords:	Sorting, Productivity Signals, Labor Market Concentration, Innovation
JEL:	J23 J24 O31
Date:	2026–04
URL:	https://d.repec.org/n?u=RePEc:crm:wpaper:26106

By:	Ugur Aytun (Department of Economics, Middle East Technical University, Ankara, Turkey); Eren Gurer (Department of Economics, Middle East Technical University, Ankara, Turkey); Erol Taymaz (Department of Economics, Middle East Technical University, Ankara, Turkey)
Abstract:	In December 2017, the government of TÂ¨ urkiye announced a comprehensive ban on the procurement of outsourced services by public institutions and mandated that all workers providing such services on-site be transitioned into permanent public positions within six months. We study the labor-market consequences of this abrupt and large-scale policy change using an administrative, linked employerâ€“employee dataset. We find that workers who transitioned into public employment experienced higher wages and improved job security. At the firm level, private service providers with greater exposure to the reform faced higher exit rates and, if they survived, declines in employment, productivity, and profitability. In contrast, municipal-owned enterprises that internalized service provision became more productive and profitable. We also document modest positive wage spillovers in local labor markets. Overall, our results suggest that the outsourcing ban reallocated rents away from private service providers toward workers and public employers.
Keywords:	public employment, outsourcing reform, labor market spillovers, firm dynamics, productivity
JEL:	J31 J38 J62 L33
Date:	2026–04
URL:	https://d.repec.org/n?u=RePEc:met:wpaper:2602

By:	Susan Athey; Lisa K. Simon; Oskar Nordström Skans; Johan Vikström; Yaroslav Yakymovych
Abstract:	Using rich Swedish administrative data, we apply causal machine learning methods to study how earnings losses after job displacement vary with observable characteristics that may be relevant for targeting policy interventions for workers. Heterogeneity in effects is as large within as across worker groups defined by age and schooling, and as large within as across establishments. A substantial portion of cross-establishment heterogeneity can be explained by industry and local labor market characteristics, suggesting a role for place- and industry-based targeting. The largest losses are concentrated among already vulnerable workers, indicating that well-designed targeting policies can improve both efficiency and equity.
Keywords:	Plant closures, heterogeneous effects, GRF
JEL:	J65 J21 J31 C45
Date:	2026–03
URL:	https://d.repec.org/n?u=RePEc:crm:wpaper:26075

By:	Gabriele Lucchetti
Abstract:	I document that the city-size earnings premium is smaller for immigrants than for natives, driven by low-income-country immigrants, who sort less into cognitive occupations in larger cities. I interpret these facts through a spatial equilibrium model with heterogeneous human capital, amenities, and local earnings wedges. Local earnings wedges are the main channel behind the immigrant-native gap in the city-size earnings premium. Removing all immigrant-native differences closes 62 percent of the immigrant-native earnings gap but widens the spatial earnings gap by 20 percent. Expanding college immigration narrows the workers' earnings gap without widening the spatial earnings gap.
Keywords:	Immigrant Earnings Gap, City-Size Earnings Premium, Spatial Equilibrium, Occupational Sorting, Human Capital
JEL:	F22 J24 J31 J61 R13
Date:	2026–03
URL:	https://d.repec.org/n?u=RePEc:crm:wpaper:26082

By:	Gaia Dossi; Marta Morando
Abstract:	We link U.S. patent and inventor records to individual voter register files and map politically polarized policy issues to related technologies. Compared to Republicans, Democrats are one-third more likely to patent technologies addressing climate-change mitigation or women's reproductive health, and one-third less likely to patent weapons and related technologies. These gaps are not explained by differences in inventive ability or by sorting across organizations or teams. Party-technology alignment has strengthened over the past two decades, a period of rising political polarization in U.S. society. Technology diffusion is also politically polarized: Democrats are more likely than Republicans to cite aligned technologies and less likely to cite misaligned ones. Together, these findings are consistent with political polarization and societal views being important drivers of the direction and diffusion of technological change and operating, at least in part, through inventors' technology choices, with implications for innovation policy.
Keywords:	Diffusion, Innovation, Partisanship, Polarization, Technology
JEL:	D72 I10 J24 O31 O33 O44 P00
Date:	2026–03
URL:	https://d.repec.org/n?u=RePEc:crm:wpaper:26064

By:	Giuseppe Pulito; Mariola Pytlikova; Sarah Schroeder; Magnus Lodefalk
Abstract:	Using two waves of nationally representative Danish firm surveys linked to employer-employee administrative registers, we study how adoption varies across artificial intelligence (AI) and related advanced technologies. We show that AI adoption is highly technology-specific. While firm size and digital infrastructure predict adoption broadly, workforce composition operates through distinct channels: STEM-educated workforces predict core AI adoption, whereas non-STEM university-educated workforces are associated with generative AI adoption, indicating different human capital complementarities. The factors associated with adoption differ from those predicting deployment breadth: firm size and digital maturity matter for both, whereas workforce composition primarily predicts adoption alone. Machine learning and natural language processing are deployed across multiple business functions, whereas other advanced technologies remain concentrated in specific operational domains. Individual-level evidence provides a foundation for these patterns, with awareness of workplace AI usage concentrated among managers and high-skilled workers. Self-reported AI knowledge is higher among younger and more educated individuals. Finally, commonly used occupational AI exposure measures vary substantially in their ability to predict observed adoption, with benchmark-based measures outperforming patent-based and LLM-focused alternatives. These findings show that treating AI as a monolithic category obscures economically meaningful variation in who adopts, what they deploy, and how well existing measures capture it.
Keywords:	Artificial Intelligence; Technology Adoption; Digitalisation; Human capital; AI Exposure Measures
JEL:	D24 J23 J62 O33
Date:	2026–03
URL:	https://d.repec.org/n?u=RePEc:crm:wpaper:26089

By:	Mark Borgschulte; Heepyung Cho; Darren Lubotsky
Abstract:	We examine the differential effects of minimum wages on immigrant and native workers in the United States. We find that minimum wage increases lead to reduced hours of work among immigrants with no effect on their employment. The effects are concentrated among recently-arrived, likely-undocumented workers in high turnover industries. Native workers show no such response, even when examining native subgroups with similar characteristics to the most affected immigrants. We conclude that affected immigrant labor markets feature low-surplus, low-investment employment relationships with flexible hours, but they are embedded in labor markets where replacement is unusually costly.
Keywords:	Immigrants, minimum wage, employment, hours of work, labor hoarding
JEL:	J08 J15 J38 J42 J61
Date:	2026–04
URL:	https://d.repec.org/n?u=RePEc:crm:wpaper:26103

By:	Rune Vejlin; Hans Sigaard; Michael Svarer; Anne Katrine Borgbjerg
Abstract:	This paper estimates the effect of a reform-induced increase in the early retirement age (ERA) on labor supply, health, and healthcare utilization using detailed Danish administrative data and a regression discontinuity design. We show that while raising the ERA successfully increased employment, it also led to spillovers into other public transfers and increased the number of self-supporting individuals. We find that the increased ERA led to small and insignificant effects on GP visits and the use of painkillers, as well as borderline significant, small positive effects on the use of antidepressants and CVD medicine. Further analysis shows that individuals who were employed due to the reform had lower pre-reform income and wealth, while the individuals who were not employed despite being affected by the reform were characterized by worse health before the reform announcement. We argue that possibilities for exiting employment serve as a potentially important mitigating mechanism for health and healthcare utilization effects by sorting vulnerable individuals out of employment.
Keywords:	retirement reforms, health, healthcare utilization
JEL:	I18 J18 J26
Date:	2026–02
URL:	https://d.repec.org/n?u=RePEc:crm:wpaper:26046

By:	Xiao Ma; Alejandro Nakab; Camila Navajas-Ahumada; Daniela Vidart
Abstract:	Women experience slower wage growth than men over their lifetimes, a gap often attributed to the “motherhood wage penalty, ” as childbearing reduces earnings. This paper links this penalty to differences in human capital using a pseudo-event study of first childbirth in Europe to document a “motherhood training penalty.” Before parenthood, full-time male and female workers exhibit similar on-the-job training trends, but their trajectories diverge afterward. In the first 1–3 years of parenthood, women are 17%–21% less likely to train, compared to a 1%–5% decline for men. Additional evidence suggests this gap reflects employers’ lower willingness to finance training for mothers, and that it is larger in countries with higher childcare costs and weaker government support for training.
Keywords:	On-the-Job Training, Human Capital Accumulation, Lifecycle Wage Growth, Gender Gaps
JEL:	J24 J16 M53
Date:	2026–03
URL:	https://d.repec.org/n?u=RePEc:udt:wpgobi:wp_gob_2026_04

By:	Bonilla, Leonardo (Banco de la República); Munoz-Morales, Juan (IESEG School of Management); Zarate, Roman (UCSD)
Abstract:	This paper examines how import liberalization affects labor markets when labor and intermediate inputs can act as complements or substitutes. We incorporate a CES production function into a dynamic quantitative trade model and show that the labor market effects of imports depend on the elasticity of substitution between labor and intermediate inputs, which varies across sectors. Exploiting exogenous tariff reductions in Colombia and applying a difference-in-differences strategy, we separate the reduced-form effects of trade into an input shock and a competition shock. Import competition reduces the wage bill across sectors, whereas cheaper intermediate inputs increase it. This increase is driven by the service sector, with imprecise effects in manufacturing and an opposite-sign effect in agriculture. Combining the model with the reduced-form parameters, we implement indirect inference to recover sector-specific elasticities of substitution and find that labor and intermediates are substitutes in agriculture and manufacturing but complements in services. Allowing for a more flexible production structure meaningfully changes the labor market response to trade.
Keywords:	trade liberalization, import competition, foreign inputs, employment, earnings
JEL:	J21 J30 F14 O15
Date:	2026–04
URL:	https://d.repec.org/n?u=RePEc:iza:izadps:dp18558

By:	Checchi, Daniele (University of Milan); Defraja, Gianni (University of Nottigham); Marra, Alfredo (University of Milano-Bicocca); Verzillo, Stefano (European Commission Joint Research Centre (JRC))
Abstract:	We investigate the impact of a reform introduced in Italy in 2010 (Law 240/2010, known as the Gelmini reform) that aimed to liberalise external economic activities for academics, whose pay had been effectively frozen for the previous seven years. The reform partially liberalised remunerated external activities while simultaneously restructuring university governance to expand institutional autonomy and strengthen central assessment of academic performance. Using administrative data from a representative sample of Italian academics, we compute both extensive (participation) and intensive (incidence relative to salary) margins of external activities. Our main finding is that the reform did not alter the behaviour of Italian academics. Among the possible explanations, we suggest the substantial leeway to restrict academics' remunerated external activities enjoyed by universities' administration under the new governance framework and the interpretations in the Court of Auditors, which also restricted the range of permissible activities.
Keywords:	academics, monetary incentive, Italy
JEL:	J33 K31
Date:	2026–04
URL:	https://d.repec.org/n?u=RePEc:iza:izadps:dp18561

By:	Pauline Carry
Abstract:	This paper examines the effects of working time regulations on the allocation of workers and hours. I exploit a unique reform introducing a minimum workweek of 24 hours in France in 2014, affecting 15% of jobs. Drawing on administrative data and an event study design, I find a firm-level reduction in total hours worked, showing imperfect substitutability between workers and hours. The effects differ by gender: women working part-time were replaced by men working longer hours. Importantly, workers also reallocate between firms. To quantify the aggregate impact accounting for these effects, I build and estimate a search and matching model with firm and worker heterogeneity. Overall, the minimum workweek reduced employment by 1.4%, largely driven by women, and decreased total hours by 0.5%.
Keywords:	Working time regulations, Hours of work, Reallocation effects, Gender inequality
JEL:	J08 J23 J41 E24
Date:	2026–03
URL:	https://d.repec.org/n?u=RePEc:crm:wpaper:26071

By:	Anna Pestova; Alexander Popov
Abstract:	We document persistent human capital scarring among the children of homeowners who reached college age during the 2008–2011 housing bust. Negative shocks to parental housing wealth substantially reduced college attendance among first-year college-age children of homeowners, relative to their counterparts from renter households. In regions experiencing the largest declines in housing wealth, the educational gap between the offspring of homeowners and renters persisted for at least a decade. The shortfall in human capital accumulation translated into lower long-run employability, particularly in education-intensive sectors, and resulted in lower earnings among the affected cohort.
Keywords:	Homeownership, housing wealth, human capital, housing boom-bust episodes
JEL:	I24 E32 J24
Date:	2026–03
URL:	https://d.repec.org/n?u=RePEc:cer:papers:wp817

By:	Francesco Del Prato; Salvatore Lattanzio
Abstract:	Men experience workplace injuries at roughly twice the rate of women. We study whether compensating differentials for injury risk contribute to gender differences in firm pay policies. We develop a search model that microfounds an AKM wage equation, decomposing firm pay effects into productivity and injury-risk components. Using Italian matched employer-employee data with individual injury records, we estimate gender-specific firm wage effects and firm-level injury risk. We find that injury-related channels account for 8 percent of the gender gap in firm wage effects, rising to 17 percent in manufacturing. While women receive only 86 percent of men's wage response to firm-level injury risk, conditioning on broad occupation eliminates this within-firm disparity. This indicates that the injury channel reflects sorting across firms and occupational allocation within firms, rather than differential pricing of identical risk.
Keywords:	Gender wage gap, workplace injuries, compensating differentials, AKM, rent sharing
JEL:	J16 J28 J31 J64 J71
Date:	2026–03
URL:	https://d.repec.org/n?u=RePEc:crm:wpaper:26081

By:	Bloom, Nicholas (Stanford University); Dahl, Gordon (University of California, San Diego); Rooth, Dan-Olof (Stockholm University)
Abstract:	There has been a dramatic rise in disability employment since the pandemic. At the same time, work from home (WFH) has risen four-fold. This paper asks whether the two are causally related. Controlling for compositional changes and labor market tightness, a 1 percentage point increase in WFH increases full-time employment by 1.0% for individuals with a physical disability. The postpandemic increase in working from home explains 68%-85% of the rise in full-time employment. Wage data suggests that WFH increased the supply of workers with a physical disability, likely by reducing commuting costs and enabling better control of working conditions.
Keywords:	work from home, disability employment
JEL:	J14 J42
Date:	2026–04
URL:	https://d.repec.org/n?u=RePEc:iza:izadps:dp18555

By:	Anna Stansbury; Kyra Rodriguez
Abstract:	Unlike gender or race, class background is rarely a focus of research on career progression, or of DEI efforts in elite occupations. Should it be? In this paper we document a large class gap in career progression in one occupation - US tenure-track academia - using parental education to proxy for class background. First-generation college graduates are 10% less likely to be tenured at an R1, are tenured at institutions ranked 11% lower, earn 3% less, and report 5% lower job satisfaction, than their former PhD classmates (from the same institution and field) with a parent with a non-PhD graduate degree. Neither selection out of academia nor different preferences explain this gap; differential research productivity also plays little role. Instead, likely drivers are differences in cultural and social capital. We also find a class gap in career progression for PhDs who work in industry, suggesting this phenomenon generalizes outside academia.
Keywords:	class; socioeconomic background; mobility; discrimination
JEL:	J7 J44 J31
Date:	2026–02
URL:	https://d.repec.org/n?u=RePEc:crm:wpaper:26042

By:	Adhvaryu, Achyuta; Nyshadham, Anant; Tamayo, Jorge Andrés; Molina, Teresa; Bhalotra, Sonia
Abstract:	Using administrative and survey data from a large restaurant chain in Colombia, we documented gender differences in the managerial approaches most strongly associated with performance. Based on these findings, we designed two customized curricula: a relationship-based curriculum highlighting characteristics exemplified by high-performing female managers and a task- and metric-based curriculum emphasizing characteristics exemplified by high-performing male managers. Both curricula also included training on universally effective practices. We then implemented a randomized controlled trial in which stores were assigned to a control group, a relationship curriculum group, or a task and metric curriculum group. Managers assigned to the relationship-focused curriculum demonstrate significantly larger improvements on relationship-related questions than do those assigned to the task-focused curriculum, while the reverse is true for task-related questions. Importantly, these targeted gains do not come at the expense of general learning: managers in both treatment groups also demonstrate improvements in questions covering content common to both curricula. Follow-up data collection will measure treatment effects on store- and worker-level outcomes.
JEL:	L20 M50 J24 L66
Date:	2026–04
URL:	https://d.repec.org/n?u=RePEc:idb:brikps:14570

By:	Boeri, Tito (Bocconi University); Crescioli, Tommaso (Prometeia); Garnero, Andrea (OECD); Luisetto, Lorenzo (Cleveland State University)
Abstract:	Can collective bargaining mitigate monopsony power? This paper addresses this question by examining how the regulation of noncompete agreements for employees by collective agreements affects firm-level markdowns in the French manufacturing sector. Using a staggered difference-in-differences approach, we find that the regulation of noncompetes set by collective agreements leads to a 1.3%-2.2% reduction in markdowns on average. The effect grows over time and is more pronounced for smaller, less productive firms that pay lower wages. Studying a landmark decision of the French Supreme Court that introduced the obligation to have a compensation to consider a noncompete enforceable, we find a significant complementarity between the regulation of noncompetes at the national level (e.g., via case law) and sectoral collective bargaining. By enhancing compliance or imposing further restrictions, collective bargaining can serve as an effective tool to regulate the use of noncompete agreements.
Keywords:	monoposony, unions, noncompetes
JEL:	J42 J51 J53 J58 J31
Date:	2026–04
URL:	https://d.repec.org/n?u=RePEc:iza:izadps:dp18564

By:	Pereira, João; Ramos, Raul; Martins, Pedro S.
Abstract:	We propose a simple and novel approach to estimate the cyclicality of gaps using cross-sectional data. Our method controls for composition effects in contexts in which panel data cannot be leveraged. Our method also provides useful insights for assessing common trends in difference-in-differences analyses. We illustrate the approach studying the gender wag gap (GWG) in Portugal, finding that it is generally procyclical, except at the top of the distribution. This procyclicality stems from the unexplained component of the GWG rather than from differences in observed characteristics. The results also do not support the hypothesis that GWG cyclicality is driven by discrimination.
Keywords:	Wage decompositions, Gender gaps, Wage cyclicality, Discrimination
JEL:	J31 E32 J16 J71 J82
Date:	2026
URL:	https://d.repec.org/n?u=RePEc:zbw:glodps:1746

By:	Martin Olsson; Fredrik Heyman
Abstract:	This paper examines how automation shapes intergenerational income mobility. Using Swedish register data on parents and children from 1985 to 2019, we study how parental exposure to robots at the occupational and industry level during the 1990s affected children's outcomes up to thirty years later. To address selection, we match parents on detailed worker, firm, and family characteristics and complement this with firm-level variation based on robot and broader automation imports. We also employ two IV strategies that leverage exogenous variation in automation adoption: one based on foreign industry-level robot adoption, and another exploiting differences in managerial education at the firm level. Our results show that parental exposure to robotization and automation reduces children's income and upward mobility, and leads to worse long-run labor market and educational outcomes. These effects are concentrated among low-income families. Evidence suggests that parental labor market shocks and financial strain are key mechanisms. Taken together, the findings indicate that technological change can reduce intergenerational mobility and contribute to long-run inequality.
Keywords:	Intergenerational Mobility; Robots; Automation; Inequality
JEL:	J31 J62 O33
Date:	2026–02
URL:	https://d.repec.org/n?u=RePEc:crm:wpaper:26047